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At least 415 records · Page 23Linked to original sources

USGS2021

Grand challenges of hydrologic modeling for food-energy-water nexus security in high mountain Asia

Climate-influenced changes in hydrology affect water-food-energy security that may impact up to two billion people downstream of the High Mountain Asia (HMA) region. Changes in water supply affect energy, industry, transportation, and ecosystems (agriculture, fisheries) and as a result, also affect the region's social, environmental, and economic fabrics. Sustaining the highly interconnected food-energy-water nexus (FEWN) will be a fundamental and increasing challenge under a changing climate regime. High variability in topography and distribution of glaciated and snow-covered areas in the HMA region, and scarcity of high resolution ( in-situ ) data make it difficult to model and project climate change impacts on individual watersheds. We lack basic understanding of the spatial and temporal variations in climate, surface impurities in snow and ice such as black carbon and dust that alter surface albedo, and glacier mass balance and dynamics. These knowledge gaps create challenges in predicting where and when the impact of changes in river flow will be the most significant economically and ecologically. In response to these challenges, the United States National Aeronautics and Space Administration (NASA) established the High Mountain Asia Team (HiMAT) in 2016 to conduct research to address knowledge gaps. This paper summarizes some of the advances HiMAT made over the past 5 years, highlights the scientific challenges in improving our understanding of the hydrology of the HMA region, and introduces an integrated assessment framework to assess the impacts of climate changes on the FEWN for the HMA region. The framework, developed under a NASA HMA project, links climate models, hydrology, hydropower, fish biology, and economic analysis. The framework could be applied to develop scientific understanding of spatio-temporal variability in water availability and the resultant downstream impacts on the FEWN to support water resource management under a changing climate regime.

Frontiers in Water↗

USGS2007

Coupling alongshore variations in wave energy to beach morphologic change using the SWAN wave model at Ocean Beach, San Francisco, CA

Coastal managers have faced increasing pressure to manage their resources wisely over the last century as a result of heightened development and changing environmental forcing. It is crucial to understand seasonal changes in beach volume and shape in order to identify areas vulnerable to accelerated erosion. Shepard (1950) was among the first to quantify seasonal beach cycles. Sonu and Van Beek (1971) and Wright et al. (1985) described commonly occurring beach states. Most studies utilize widest spaced 2-D cross shore profiles or shorelines extracted from aerial photographs (e.g. Winant et al. 1975; Aubrey, 1979, Aubrey and Ross, 1985; Larson and Kraus, 1994; Jimenez et al., 1977; Lacey and Peck, 1998; Guillen et al., 1999; Norcorss et al., 2002) to analyzed systematic changes in beach evolution. But with the exception of established field stations, such as Duck, NC (Birkemeier and Mason, 1984), ans Hazaki Oceanographical Research Station (HORS) in Japan (Katoh, 1997), there are very few beach change data sets with high temporal and spatial resolutions (e.g. Dail et al., 2000; Ruggiero et al., 2005; Yates et al., in press). Comprehensive sets of nearshore morphological data and local in situ measurements outside of these field stations are very rare and virtually non-existent high-energy coasts. Studied that have attempted to relate wave statistics to beach morphology change require some knowledge of the nearshore wave climate, and have had limited success using offshore measurement (Sonu and Van Beek, 1971; Dail et al., 2000). The primary objective of this study is to qualitatively compare spatially variable nearshore wave predictions to beach change measurements in order to understand the processes responsible for a persistent erosion 'hotspot' at Ocean Beach, San Francisco, CA. Local wave measurements are used to calibrate and validate a wave model that provides nearshore wave prediction along the beach. The model is run for thousands of binned offshore wave conditions to help isolate the effects of offshore wave direction and period on nearshore wave predictions. Alongshore varying average beach change statistics are computed at specific profile locations from topographic beach surveys and lidar data. The study area is located in the San Francisco Bight in central California. Ocean Beach is a seven kilometer long north-south trending sandy coastline located just south of the entrance to the San Francisco Bay Estuary (Figure 1). It contains an erosion hotspot in the southern part of the beach which has resulted in damage to local infrastructure and is the cause of continued concern. A wide range of field data collection and numerical modeling efforts have been focused here as part of the United States Geological Survey's (USGS) San Francisco Bight Coastal Processes Study, which began in October 2003 and represents the first comprehensive study of coastal processes at the mouth of San Francisco Bay. Ocean Beach is exposed to very strong tidal flows, with measured currents often in excess of 1 m/s at the north end of the beach. Current profiler measurements indicate that current magnitudes are greater in the northern portion of the beach, while wave energy is greater in the southern portion where erosion problems are greatest (Barnard et al., 2007). The sub-aerial beach volume fluctuates seasonally over a maximum envelope of 400,000 m 3 for the seven kilometer stretch (Barnard et al, 2007). The wave climate in the region is dominated by an abundance of low frequency energy (greater than 20 s period) and prevailing northwest incident wave angles. The application of a wave model to the region is further complicated by the presence of the Farallon Islands 40 kilometers west, and a massive ebb tidal delta at the mouth of San Francisco Bay (~150 km 2 ), which creates complicated refraction patterns as wave energy moves from offshore Ocean Beach; however the cost and threat of the energetic nearshore environment have limited the temporal and spatial resolution of these measurements. Applying numerical models to predict wave and current patterns along the beach can help supplement the filed data that exists and provide opportunities to make prediction about the impacts of changing environmental forcing.

California↗

USGS2021

Luquillo Experimental Forest: Catchment science in the montane tropics

Catchments in the Luquillo Experimental Forest (LEF) of Puerto Rico are warm, wet and tropical with steep elevational relief creating gradients in temperature and rainfall. Long-term objectives of research at the site are to understand how changing climate and disturbance regimes alter hydrological and biogeochemical processes in the montane tropics and to provide information critical for managing and conserving tropical forest ecosystems globally. Measurements of hydrology and meteorology span decades, and currently include temperature, humidity, precipitation, cloud base level, throughfall, groundwater table elevation and stream discharge. The chemistry of rain, throughfall, and streams is measured weekly and lysimeters and wells are sampled monthly to quarterly. Multiple data sets document the effects of major hurricanes including Hugo (1989), Georges (1998) and Maria (2017) on vegetation, biota and catchment biogeochemistry and provide some of the longest available records of biogeochemical fluxes in tropical forests. Here we present an overview of the findings and the data sets that have been generated from the LEF, highlighting their importance for understanding montane tropical watersheds in the context of disturbance and global environmental change.

Luquillo Experimental Forest, Puerto Rico↗

USGS2022

In situ soil moisture sensors in undisturbed soils

Soil moisture directly affects operational hydrology, food security, ecosystem services, and the climate system. However, the adoption of soil moisture data has been slow due to inconsistent data collection, poor standardization, and typically short record duration. Soil moisture, or quantitatively volumetric soil water content (SWC), is measured using buried, in situ sensors that infer SWC from an electromagnetic response. This signal can vary considerably with local site conditions such as clay content and mineralogy, soil salinity or bulk electrical conductivity, and soil temperature; each of these can have varying impacts depending on the sensor technology., Furthermore, poor soil contact and sensor degradation can affect the quality of these readings over time. Unlike more traditional environmental sensors, there are no accepted standards, maintenance practices, or quality controls for SWC data. As such, SWC is a challenging measurement for many environmental monitoring networks to implement. Here, we attempt to establish a community-based standard of practice for in situ SWC sensors so that future research and applications have consistent guidance on site selection, sensor installation, data interpretation, and long-term maintenance of monitoring stations., The videography focuses on a multi-agency consensus of best-practices and recommendations for the installation of in situ SWC sensors. This paper presents an overview of this protocol along with the various steps essential for high-quality and long-term SWC data collection. This protocol will be of use to scientists and engineers hoping to deploy a single station or an entire network.

Journal of Visualized Experiments↗

USGS2012

Hydrologic and landscape database for the Cache and White River National Wildlife Refuges and contributing watersheds in Arkansas, Missouri, and Oklahoma

A hydrologic and landscape database was developed by the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, for the Cache River and White River National Wildlife Refuges and their contributing watersheds in Arkansas, Missouri, and Oklahoma. The database is composed of a set of ASCII files, Microsoft Access® files, Microsoft Excel® files, an Environmental Systems Research Institute (ESRI) ArcGIS® geodatabase, ESRI ArcGRID® raster datasets, and an ESRI ArcReader® published map. The database was developed as an assessment and evaluation tool to use in examining refuge-specific hydrologic patterns and trends as related to water availability for refuge ecosystems, habitats, and target species; and includes hydrologic time-series data, statistics, and hydroecological metrics that can be used to assess refuge hydrologic conditions and the availability of aquatic and riparian habitat. Landscape data that describe the refuge physiographic setting and the locations of hydrologic-data collection stations are also included in the database. Categories of landscape data include land cover, soil hydrologic characteristics, physiographic features, geographic and hydrographic boundaries, hydrographic features, regional runoff estimates, and gaging-station locations. The database geographic extent covers three hydrologic subregions—the Lower Mississippi–St Francis (0802), the Upper White (1101), and the Lower Arkansas (1111)—within which human activities, climatic variation, and hydrologic processes can potentially affect the hydrologic regime of the refuges and adjacent areas. Database construction has been automated to facilitate periodic updates with new data. The database report (1) serves as a user guide for the database, (2) describes the data-collection, data-reduction, and data-analysis methods used to construct the database, (3) provides a statistical and graphical description of the database, and (4) provides detailed information on the development of analytical techniques designed to assess water availability for ecological needs.

Arkansas;Missouri;Oklahoma↗

USGS2015

Fire activity as a function of fire–weather seasonal severity and antecedent climate across spatial scales in southern Europe and Pacific western USA

Climate has a strong influence on fire activity, varying across time and space. We analyzed the relationships between fire–weather conditions during the main fire season and antecedent water-balance conditions and fires in two Mediterranean-type regions with contrasted management histories: five southern countries of the European Union (EUMED)(all fires); the Pacific western coast of the USA (California and Oregon, PWUSA)(national forest fires). Total number of fires (≥1 ha), number of large fires (≥100 ha) and area burned were related to mean seasonal fire weather index (FWI), number of days over the 90th percentile of the FWI, and to the standardized precipitation-evapotranspiration index (SPEI) from the preceding 3 (spring) or 8 (autumn through spring) months. Calculations were made at three spatial aggregations in each area, and models related first-difference (year-to-year change) of fires and FWI/climate variables to minimize autocorrelation. An increase in mean seasonal FWI resulted in increases in the three fire variables across spatial scales in both regions. SPEI contributed little to explain fires, with few exceptions. Negative water-balance (dry) conditions from autumn through spring (SPEI8) were generally more important than positive conditions (moist) in spring (SPEI3), both of which contributed positively to fires. The R2 of the models generally improved with increasing area of aggregation. For total number of fires and area burned, the R2 of the models tended to decrease with increasing mean seasonal FWI. Thus, fires were more susceptible to change with climate variability in areas with less amenable conditions for fires (lower FWI) than in areas with higher mean FWI values. The relationships were similar in both regions, albeit weaker in PWUSA, probably due to the wider latitudinal gradient covered in PWUSA than in EUMED. The large variance explained by some of the models indicates that large-scale seasonal forecast could help anticipating fire activity in the investigated areas.

California, Oregon↗

USGS2024

A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands

Popular evapotranspiration (ET) partitioning methods make assumptions that might not be well-suited to dryland ecosystems, such as high sensitivity of plant water-use efficiency (WUE) to vapor pressure deficit (VPD). Our objectives were to (a) create an ET partitioning model that can produce fine-scale estimates of transpiration (T) in drylands, and (b) use this approach to evaluate how climate controls T and WUE across ecosystem types and timescales along a dryland aridity gradient. We developed a novel, semi-mechanistic ET partitioning method using a Bayesian approach that constrains abiotic evaporation using process-based models, and loosely constrains time-varying WUE within an autoregressive framework. We used this method to estimate daily T and weekly WUE across seven dryland ecosystem types and found that T dominates ET across the aridity gradient. Then, we applied cross-wavelet coherence analysis to evaluate the temporal coherence between focal response variables (WUE and T/ET) and environmental variables. At yearly scales, we found that WUE at less arid, higher elevation sites was primarily limited by atmospheric moisture demand, and WUE at more arid, lower elevation sites was primarily limited by moisture supply. At sub-yearly timescales, WUE and VPD were sporadically correlated. Hence, ecosystem-scale dryland WUE is not always sensitive to changes in VPD at short timescales, despite this being a common assumption in many ET partitioning models. This new ET partitioning method can be used in dryland ecosystems to better understand how climate influences physically and biologically driven water fluxes.

New Mexico↗

USGS2012

Metadata squared: enhancing its usability for volunteered geographic information and the GeoWeb

The Internet has brought many changes to the way geographic information is created and shared. One aspect that has not changed is metadata. Static spatial data quality descriptions were standardized in the mid-1990s and cannot accommodate the current climate of data creation where nonexperts are using mobile phones and other location-based devices on a continuous basis to contribute data to Internet mapping platforms. The usability of standard geospatial metadata is being questioned by academics and neogeographers alike. This chapter analyzes current discussions of metadata to demonstrate how the media shift that is occurring has affected requirements for metadata. Two case studies of metadata use are presented—online sharing of environmental information through a regional spatial data infrastructure in the early 2000s, and new types of metadata that are being used today in OpenStreetMap, a map of the world created entirely by volunteers. Changes in metadata requirements are examined for usability, the ease with which metadata supports coproduction of data by communities of users, how metadata enhances findability, and how the relationship between metadata and data has changed. We argue that traditional metadata associated with spatial data infrastructures is inadequate and suggest several research avenues to make this type of metadata more interactive and effective in the GeoWeb.

Book chapter↗

USGS2024

Tissue distribution and temporal and spatial assessment of per- and polyfluoroalkyl substances (PFAS) in smallmouth bass (Micropterus dolomieu) in the mid-Atlantic United States

Per- and polyfluoroalkyl substances (PFAS) have become an environmental issue worldwide. A first step to assessing potential adverse effects on fish populations is to determine if concentrations of concern are present in a region and if so, in which watersheds. Hence, plasma from adult smallmouth bass Micropterus dolomieu collected at 10 sites within 4 river systems in the mid-Atlantic region of the United States, from 2014 to 2019, was analyzed for 13 PFAS. These analyses were directed at better understanding the presence and associations with land use attributes in an important sportfish. Four substances, PFOS, PFDA, PFUnA, and PFDoA, were detected in every plasma sample, with PFOS having the highest concentrations. Sites with mean plasma concentrations of PFOS below 100 ng/ml had the lowest percentage of developed landcover in the upstream catchments. Sites with moderate plasma concentrations (mean PFOS concentrations between 220 and 240 ng/ml) had low (< 7.0) percentages of developed land use but high (> 30) percentages of agricultural land use. Sites with mean plasma concentrations of PFOS > 350 ng/ml had the highest percentage of developed land use and the highest number PFAS facilities that included military installations and airports. Four of the sites were part of a long-term monitoring project, and PFAS concentrations of samples collected in spring 2017, 2018, and 2019 were compared. Significant annual differences in plasma concentrations were noted that may relate to sources and climatic factors. Samples were also collected at two sites for tissue (plasma, whole blood, liver, gonad, muscle) distribution analyses with an expanded analyte list of 28 PFAS. Relative tissue distributions were not consistent even within one species of similar ages. Although the long-chained legacy PFAS were generally detected more frequently and at higher concentrations, emerging compounds such as 6:2 FTS and GEN X were detected in a variety of tissues.

Delaware, Maryland, New York, Pennsylvania, Virgin↗

USGS2024

2023-2024 Coastal sage scrub and chaparral community monitoring for western San Diego County

Western San Diego County is dominated by shrublands supporting biologically diverse native plant and animal communities. Widespread urbanization has led to regional habitat loss and fragmentation, and many species in these shrubland communities are rare, threatened, or endangered. Large-scale, multiple-species conservation planning has resulted in a regional preserve system that focuses on these shrubland communities. Several large-scale threats are leading to type conversion from shrub-dominated to non-native invasive annual grass-dominated vegetation. To understand the changes that are occurring to native shrublands, we have developed a vegetation monitoring program with several components at multiple spatial scales, focused on quantifying coastal sage scrub (CSS) and chaparral vegetation community characteristics. Several drivers of change associated with type conversion of native shrubland to non-native annual grassland have been identified by previous research including increasing fire frequency, nitrogen deposition from air pollution, and prolonged and intense drought associated with changing climate. Loss of ecological integrity indices have been proposed as useful measures of the threat of degradation and type conversion of shrublands in San Diego County. For this study, ecological integrity is defined as a system’s ability to maintain species’ relationships and functions comparable to natural habitat in the region. Previous studies have identified the percent cover of invasive non-native annual grasses as a proxy for overall ecological degradation (loss of integrity) that is consistent across native taxonomic groups. Increased cover of non-native grass is associated with lower integrity of the shrubland vegetation community as shrub-associated plant and animal species are replaced by species preferring grassy and disturbed habitats. The objectives of this CSS and chaparral vegetation community monitoring plan are to: 1) Determine the distribution, composition, structure, and integrity of CSS and chaparral vegetation communities on conserved lands in western San Diego County, 2) Identify whether these attributes of the vegetation communities are changing over time, and 3) Evaluate relationships of known drivers of change (threats) and environmental factors in association with changes in vegetation community attributes. The CSS and chaparral vegetation community monitoring program is divided into four components: 1) vegetation mapping, 2) GIS/remote sensing office analysis of landscape-scale data, 3) permanent field plots using Unmanned Aerial Systems (UAS) and field data collections, and 4) animal and target species surveys and rapid assessment protocols. The first component, which is not detailed in this vegetation monitoring plan, aims to map vegetation communities every 10-15 years based on a classification developed for western San Diego County. High resolution aerial imagery will be used to update the 2012 vegetation map and expand it across the entire study area. The second component uses remote sensing models to annually track ecological integrity of shrublands across the study area and will include a map of areas of change and areas of stability over several decades. These landscape-scale integrity classifications will be used to analyze ecological processes, threats, and abiotic factors relative to changes to shrubland ecological integrity over time and space. The third component includes field surveys of 100 permanent plots across areas historically mapped as shrublands. Surveys in the 1930s mapped vegetation types using plot data. By using this historical classification map, we included areas that have already type-converted from shrubland to non-native annual grassland. The plots were split between CSS (55 plots) and chaparral (45 plots) and stratified into four geographical eco-subregions to guarantee coverage over small patches of habitat along the heavily developed coast. Surveys will include the collection of UAS imagery at a very high resolution. Species-level identifications will be made from the imagery based on a plant list compiled of all species detected in the plot during a thorough field survey by botanists, combined with geo-referenced samples of plant species locations. In addition, herbaceous cover will be estimated in the field using nine 1-m diameter circles (one per subplot) to obtain ocular estimates of cover for each plant species within the circle frame. Soil samples will be collected and analyzed for important element compositions. These data will be analyzed to evaluate plot-level ecological integrity based on species composition and cover. Repeated monitoring will allow evaluation of changes in vegetation attributes over time with known drivers or threats and other environmental factors. In addition, analyses will focus on indicator species and various measures of biological diversity for the vegetation communities. Finally, animal species and rare plants will be assessed using either taxa-based rapid assessment protocols or specialized species-specific protocols for rarer species. The purpose of these assessments is to document the status, habitat, and threat covariates of specific species and confirm the species composition and diversity of animal taxonomic groups (e.g., pollinators). Diversity and abundance of animal species at vegetation plots will be used to refine measures and thresholds of ecological integrity. Rapid assessment protocols for animal taxonomic groups can include multiple detection methods such as, camera traps, cover boards, and bird point counts. Pollinators will be monitored at plots using a protocol currently being developed in conjunction with but separately from this plan. Target rare plant species will be monitored using the regional Inspect and Manage (IMG) protocol that measures the status of rare plant occurrences and habitat and threat covariates over time. Species-specific animal survey methods will be refined as these species are prioritized for future monitoring. The goal of this monitoring program is to classify CSS and chaparral vegetation community integrity, identify areas of degradation across western San Diego County, and characterize drivers, and environmental factors associated with loss of ecological integrity. A combination of vegetation mapping, landscape-scale remote sensing, and field plots will be used to address all the aspects of our research questions. Data compiled and collected will be available to conservation partners to help inform future management decisions.

California↗

USGS2003

Orbital control of low-latitude seasonality during the Eemian

We used Sr/Ca and stable isotope data from well dated and preserved corals from the northeastern Caribbean to determine the seasonal environmental conditions for four continuous years during the Eemian, the last time the Earth was in a prolonged warm phase. We determined that the seasonal range in SST during the Eemian was 25°–30° C. This is ∼1–2° larger than at present and caused primarily by winter cooling and, only to a small degree, by summer warming. As climate modeling studies indicate, the bias towards colder winters can be explained by changes in low latitude insolation induced by altered orbital parameters, modulated by atmospheric CO 2 levels that were lower than today. Milankovitch forcing at higher latitudes was probably less important.

Geophysical Research Letters↗

USGS2016

Science to support aquatic animal health

Healthy aquatic ecosystems are home to a diversity of plants, invertebrates, fish and wildlife. Aquatic animal populations face unprecedented threats to their health and survival from climate change, water shortages, habitat alteration, invasive species and environmental contaminants. These environmental stressors can directly impact the prevalence and severity of disease in aquatic populations. For example, periodic fish kills in the upper Chesapeake Bay Watershed are associated with many different opportunistic pathogens that proliferate in stressed fish populations. An estimated 80 percent of endangered juvenile Puget Sound steelhead trout die within two weeks of entering the marine environment, and a role for disease in these losses is being investigated. The introduction of viral hemorrhagic septicemia virus (VHSV) into the Great Lakes—a fishery worth an estimated 7 billion dollars annually—resulted in widespread fish die-offs and virus detections in 28 different fish species. Millions of dying sea stars along the west coast of North America have led to investigations into sea star wasting disease. U.S. Geological Survey (USGS) scientists are assisting managers with these issues through ecological investigations of aquatic animal diseases, field surveillance, and research to promote the development of mitigation strategies.

Fact Sheet↗

USGS2017

Quantifying drivers of wild pig movement across multiple spatial and temporal scales

Background The movement behavior of an animal is determined by extrinsic and intrinsic factors that operate at multiple spatio-temporal scales, yet much of our knowledge of animal movement comes from studies that examine only one or two scales concurrently. Understanding the drivers of animal movement across multiple scales is crucial for understanding the fundamentals of movement ecology, predicting changes in distribution, describing disease dynamics, and identifying efficient methods of wildlife conservation and management. Methods We obtained over 400,000 GPS locations of wild pigs from 13 different studies spanning six states in southern U.S.A., and quantified movement rates and home range size within a single analytical framework. We used a generalized additive mixed model framework to quantify the effects of five broad predictor categories on movement: individual-level attributes, geographic factors, landscape attributes, meteorological conditions, and temporal variables. We examined effects of predictors across three temporal scales: daily, monthly, and using all data during the study period. We considered both local environmental factors such as daily weather data and distance to various resources on the landscape, as well as factors acting at a broader spatial scale such as ecoregion and season. Results We found meteorological variables (temperature and pressure), landscape features (distance to water sources), a broad-scale geographic factor (ecoregion), and individual-level characteristics (sex-age class), drove wild pig movement across all scales, but both the magnitude and shape of covariate relationships to movement differed across temporal scales. Conclusions The analytical framework we present can be used to assess movement patterns arising from multiple data sources for a range of species while accounting for spatio-temporal correlations. Our analyses show the magnitude by which reaction norms can change based on the temporal scale of response data, illustrating the importance of appropriately defining temporal scales of both the movement response and covariates depending on the intended implications of research (e.g., predicting effects of movement due to climate change versus planning local-scale management). We argue that consideration of multiple spatial scales within the same framework (rather than comparing across separate studies post-hoc) gives a more accurate quantification of cross-scale spatial effects by appropriately accounting for error correlation.

Movement Ecology↗

USGS2011

Aeolian and fluvial processes in dryland regions: The need for integrated studies

Aeolian and fluvial processes play a fundamental role in dryland regions of the world and have important environmental and ecological consequences from local to global scales. Although both processes operate over similar spatial and temporal scales and are likely strongly coupled in many dryland systems, aeolian and fluvial processes have traditionally been studied separately, making it difficult to assess their relative importance in drylands, as well as their potential for synergistic interaction. Land degradation by accelerated wind and water erosion is a major problem throughout the world's drylands, and although recent studies suggest that these processes likely interact across broad spatial and temporal scales to amplify the transport of soil resources from and within drylands, many researchers and land managers continue to view them as separate and unrelated processes. Here, we illustrate how aeolian and fluvial sediment transport is coupled at multiple spatial and temporal scales and highlight the need for these interrelated processes to be studied from a more integrated perspective that crosses traditional disciplinary boundaries. Special attention is given to how the growing threat of climate change and land‐use disturbance will influence linkages between aeolian and fluvial processes in the future. We also present emerging directions for interdisciplinary needs within the aeolian and fluvial research communities that call for better integration across a broad range of traditional disciplines such as ecology, biogeochemistry, agronomy, and soil conservation.

California↗

USGS2026

What is the (real) rate of soil health practice adoption? Making sense of three data sources

Conservation stakeholders looking to quantify the impact of their investments to increase soil health practice adoption over time often face challenges in interpreting practice adoption data due to discrepancies in language and results among data sources. Similarly, efforts to estimate environmental outcomes of practice adoption, such as water quality and greenhouse gas emissions, can vary depending on different practice adoption input data. To help make sense of different adoption data sources, we compared county-level adoption data for winter cover crops (WCC), no-till (NT), and reduced tillage (RT) in three areas of the United States with contrasting climates and production systems: central Illinois (CIL), southern Illinois (SIL), and western New York (WNY). We analyzed data available during 2015 through 2022 from the Operational Tillage Information System (OpTIS, remote sensing), US Census of Agriculture (AgCensus, a farmer survey), and, specifically in Illinois, the Illinois Soil Conservation Transect Survey (Transect, a roadside survey). The magnitude of differences between the datasets depended on the practice and geographic location. For example, OpTIS and AgCensus tillage data were much more similar in Illinois (average difference of less than 4 percentage points) compared to New York (average differences of 20 percentage points). Similarly, there was less variability and smaller differences between OpTIS and AgCensus WCC data in Illinois compared to WNY. AgCensus tended to report lower WCC adoption for Illinois and greater adoption in WNY compared to OpTIS. All data sources agreed that the rate of change in tillage practices is slow (mainly –1% to 1%) and that adoption of WCC is low (assuming linear growth, it could take nearly a century to reach 50% WCC adoption in CIL). Differences among the datasets were attributed to definitional inconsistencies for RT and NT and how WCC data were acquired. For example, the AgCensus asks if a WCC was planted, whereas OpTIS and Transect evaluate the presence of a standing WCC. Data sources also reflect different time periods (calendar years or crop years) and types of cropland assessed (corn [ Zea mays L.], soybean [ Glycine max {L.} Merr.], or all cropland). We propose two recommendations to improve interpretation and consistency: (1) a working group to harmonize definitions and protocols and develop educational materials for data users, and (2) a research effort that integrates different adoption data types and produces publicly available adoption data at HUC-10 and county scales. Such activities could help improve data access and utility for evidence-based conservation decision-making and enhance the accuracy of environmental models that rely on adoption data as input.

Journal of Soil and Water Conservation↗

USGS2023

Doing the same thing over and over again and getting the same result: Assessing variance in wetland invertebrate assemblages

Past efforts to explain variation of invertebrate assemblages in freshwater wetlands have been less productive than anticipated. To explore why efforts are disappointing, we assembled large invertebrate data sets from North Dakota prairie potholes, California rock pools, and Georgia Carolina bay wetlands that addressed spatial (among wetlands) and temporal (among seasons and years) variation. We anticipated that these large data-set sizes would enable robust conclusions to be drawn, and each place had unique environmental conditions that might contribute to greater explanatory power. We used statistical techniques that partitioned variation in invertebrate assemblages into spatial and/or temporal components, and that also yielded a measure of the amount of unexplained variation; Permutational Multivariate Analysis of Variation and Principal Coordinates Analysis assessed whole assemblage variation, and Analysis of Variance or Analysis of Covariance assessed variation in taxon richness, total abundances, and abundances of wide-spread individual taxa. Across all locations, variation explained by spatial and temporal factors, and unexplained variation were of comparable magnitudes (i.e., similar R 2 values of ~ 50%). Review of other published studies indicate that this pattern is widespread. The 50% or more unexplained variation is typically ignored by researchers, who instead focus on explained fractions. We argue that, besides addressing explained spatial and temporal variation in invertebrate assemblages (e.g., control by hydrology, resources, predation), efforts to understand what contributes to currently unexplained variation, that is unrelated to local spatial or temporal controls (e.g., broad climatic and biogeographic patterns, organism physiology and behavior), will lead to a fuller comprehension of how invertebrates in freshwater wetlands are controlled.

Wetlands↗

USGS2013

Demography and population status of polar bears in western Hudson Bay

We evaluated the demography and population status of the Western Hudson Bay (WH) polar bear subpopulation for the period 1984-2011, using live-recapture data from research studies and management actions, and dead-recovery data from polar bears harvested for subsistence purposes or removed during human-bear conflicts. We used a Bayesian implementation of multistate capture-recapture models, coupled with a matrix-based demographic projection model, to integrate several types of data and to incorporate sampling uncertainty, and demographic and environmental stochasticity across the polar bear life cycle. This approach allowed for estimation of a suite of vital rates, including survival and reproduction. These vital rates were used to parameterize a Bayesian population model to evaluate population trends and project potential population outcomes under different environmental scenarios. Survival of female polar bears of all age classes was significantly correlated with sea ice conditions; particularly with the timing of sea ice break-up in the spring and formation in the fall and the interaction of the two. This is consistent with previous findings linking body condition and survival of WH polar bears to environmental changes associated with climatic warming and supports the ecological dependence of polar bears on the availability of sea ice. Survival of male polar bears was not correlated with sea ice conditions. This was likely because a higher proportion of mortality for males was caused by humans rather than by natural factors. For example, approximately 73% of mortality for young male bears (i.e., 5-9 years old) was due to direct human-caused removals, largely because of sex selectivity in the subsistence harvest. The declining trend in size of the WH subpopulation over the period 1987-2004 was similar to a previous analysis (Regehr et al. 2007), suggesting consistency between the two demographic evaluations. Point estimates of abundance were somewhat lower using the updated statistical approach. It is important to recognize that the analyzed data were not collected in a manner that is optimal for estimating abundance and that the goal of the current analysis was to estimate vital rates and demographic trends. Estimates of population growth rate were also derived using a Bayesian population model based on estimated survival and reproductive rates from the multistate capture-recapture model. For the recent decade 2001-2011, the growth rate of the female segment of the population was 1.02 (95% CI = 0.98-1.06). Apparently stable to positive population growth for females may be due in large part to nonlinearity (i.e., short-term stability) in the long-term observed and forecasted trend toward earlier sea ice break-up in western Hudson Bay. The 2011 abundance estimate from this analysis was 806 bears with a 95% Bayesian credible interval of 653-984. This is lower than, but broadly consistent with, the abundance estimate of 1,030 (95% confidence interval = 745-1406) from a 2011 aerial survey (Stapleton et al. 2014). The capture-recapture and aerial survey approaches have different spatial and temporal coverage of the WH subpopulation and, consequently, the effective study population considered by each approach is different.

Report↗

USGS2018

Hydrologic characteristics of freshwater mussel habitat: novel insights from modeled flows

The ability to model freshwater stream habitat and species distributions is limited by the spatially sparse flow data available from long-term gauging stations. Flow data beyond the immediate vicinity of gauging stations would enhance our ability to explore and characterize hydrologic habitat suitability. The southeastern USA supports high aquatic biodiversity, but threats, such as landuse alteration, climate change, conflicting water-resource demands, and pollution, have led to the imperilment and legal protection of many species. The ability to distinguish suitable from unsuitable habitat conditions, including hydrologic suitability, is a key criterion for successful conservation and restoration of aquatic species. We used the example of the critically endangered Tar River Spinymussel ( Parvaspina steinstansana ) and associated species to demonstrate the value of modeled flow data (WaterFALL™) to generate novel insights into population structure and testable hypotheses regarding hydrologic suitability. With ordination models, we: 1) identified all catchments with potentially suitable hydrology, 2) identified 2 distinct hydrologic environments occupied by the Tar River Spinymussel, and 3) estimated greater hydrological habitat niche breadth of assumed surrogate species associates at the catchment scale. Our findings provide the first demonstrated application of complete, continuous, regional modeled hydrologic data to freshwater mussel distribution and management. This research highlights the utility of modeling and data-mining methods to facilitate further exploration and application of such modeled environmental conditions to inform aquatic species management. We conclude that such an approach can support landscape-scale management decisions that require spatial information at fine resolution (e.g., enhanced National Hydrology Dataset catchments) and broad extent (e.g., multiple river basins).

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